Who is Who on Ethereum? Account Labeling Using Heterophilic Graph Convolutional Network
针对以太坊交易网络的账户标注问题，提出KYC-GCN模型，通过多跳聚合器和重要性采样解决标签稀疏、网络异质性和大规模挑战，在真实数据集上达到90.2%准确率。
To combat cybercrimes and maintain financial security for the blockchain ecosystem, “know your customer” (KYC) is an essential and also challenging process due to the pseudonymity nature of blockchain technology. To unlock the potential of KYC on blockchain-based platforms like Ethereum, account labeling is a powerful means which can de-anonymize addresses by mining public transaction records. Existing studies on account labeling are mainly conducted via machine learning (ML) methods fed with hand-crafted features or graph neural networks based on the modeled transaction network. However, ML approaches based on hand-crafted features ignore the global interaction information between accounts, making it easy for criminals to evade detection. Moreover, the performance of traditional GCN methods when applied to Ethereum transaction network encounters limitations due to label sparsity, network heterophily, and large network size of the transaction network. In this article, we first analyze Ethereum accounts involved in typical businesses, in terms of both account and topological features. Then based on the analytical results, we propose a novel GCN method named know-your-customer graph convolutional network (KYC-GCN) which contains two key designs: 1) multihop aggregators and importance-based sampling are designed to tackle the dilemma between accuracy and efficiency. 2) GCN architecture is improved to explicitly capture local and more global information. Experimental results on a realistic Ethereum dataset show that the proposed KYC-GCN (90.2% accuracy, 86.2% Marco-F1) achieves state-of-the-art classification performance, and results on six benchmarks demonstrate that it yields great performance under homophily and heterophily.